Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network.

Banerjee, Sunetra; Ling, SH; Lyu, J; Su, S; Zheng, Y-P

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network.

Banerjee, Sunetra Ling, SH Lyu, J Su, S Zheng, Y-P

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 2039-2042
Issue Date:: 2020-07

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Field	Value	Language
dc.contributor.author	Banerjee, Sunetra
dc.contributor.author	Ling, SH
dc.contributor.author	Lyu, J
dc.contributor.author	Su, S
dc.contributor.author	Zheng, Y-P
dc.date	2020-07-20
dc.date.accessioned	2020-11-03T01:24:26Z
dc.date.available	2020-11-03T01:24:26Z
dc.date.issued	2020-07
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 2039-2042
dc.identifier.isbn	9781728119908
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/143711
dc.description.abstract	Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
dc.relation.ispartof	2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society
dc.relation.isbasedon	10.1109/embc44109.2020.9175673
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.mesh	Spine
dc.subject.mesh	Humans
dc.subject.mesh	Scoliosis
dc.subject.mesh	Imaging, Three-Dimensional
dc.subject.mesh	Ultrasonography
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Imaging, Three-Dimensional
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Scoliosis
dc.subject.mesh	Spine
dc.subject.mesh	Ultrasonography
dc.title	Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network.
dc.type	Conference Proceeding
utslib.citation.volume	2020
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-08-27T00:00:00+1000Z
dc.date.updated	2020-11-03T01:24:18Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-20
pubs.volume	2020

Abstract:

Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.

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http://hdl.handle.net/10453/143711